Triple
T241715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber |
E4943
|
entity |
| Predicate | offersProgram |
P178
|
FINISHED |
| Object |
Uber Rewards
Uber Rewards was a loyalty program that allowed frequent Uber riders and Uber Eats users to earn points and unlock tiered benefits such as discounts and priority support.
|
E4943
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Uber Rewards | Statement: [Uber, offersProgram, Uber Rewards]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uber Rewards Context triple: [Uber, offersProgram, Uber Rewards]
-
A.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
B.
Ventra
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
-
C.
Uber
Uber is a global ride-hailing and technology company that connects passengers with drivers through a mobile app and has expanded into food delivery and freight services.
-
D.
SmarTrip
SmarTrip is a rechargeable contactless smart card used to pay fares on the Washington, D.C. region’s public transit systems.
-
E.
MetroCard
MetroCard is a magnetic stripe payment card formerly used as the primary method for paying fares on New York City’s public transit system, including subways and buses.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Uber Rewards Triple: [Uber, offersProgram, Uber Rewards]
Generated description
Uber Rewards was a loyalty program that allowed frequent Uber riders and Uber Eats users to earn points and unlock tiered benefits such as discounts and priority support.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Uber Rewards Target entity description: Uber Rewards was a loyalty program that allowed frequent Uber riders and Uber Eats users to earn points and unlock tiered benefits such as discounts and priority support.
-
A.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
B.
Ventra
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
-
C.
Uber
chosen
Uber is a global ride-hailing and technology company that connects passengers with drivers through a mobile app and has expanded into food delivery and freight services.
-
D.
SmarTrip
SmarTrip is a rechargeable contactless smart card used to pay fares on the Washington, D.C. region’s public transit systems.
-
E.
MetroCard
MetroCard is a magnetic stripe payment card formerly used as the primary method for paying fares on New York City’s public transit system, including subways and buses.
- F. None of above.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25cee6f208190b996be4faa700910 |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36961e5688190b3a1ff61bb06233c |
completed | Feb. 28, 2026, 10:17 p.m. |
| NEDg | Description generation | batch_69a369de04048190b2dc01cc328e644d |
completed | Feb. 28, 2026, 10:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a36a48d5a88190a727fec1c25a1d5b |
completed | Feb. 28, 2026, 10:20 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.